In the residential energy sector, disaggregation is the task of itemizing energy consumption, attributing that consumption to various appliances throughout a home. Generally, disaggregation may be performed on data captured the home, without the need to enter the property.
Once the energy consumption is disaggregated, specific appliance usage or loads, and trends in customer behavior, may be identified. Presenting users with such information in an understandable format may allow users to take appropriate action to actively reduce total energy consumption. Moreover, providing itemized information per specific appliance may also permit users to determine if acquiring a new or replacement appliance (for example, through purchase, lease, or rental) would reduce energy costs sufficient to validate such price of purchase, lease, or rental. Accurate disaggregation may enable personalized and actionable insights to be presented to a customer, which may positively influence customer engagement as well as sentiment towards the energy-providing utility.
Typically, a software analysis is performed on past data collected Therefore such prior art techniques may be useful in breaking down the energy usage or itemizing the electric energy bill post-consumption, but fail to provide near real-time information that may immediately empower users to modify their energy usage. With regard to appliances such as heating or air conditioning—for which usage is based upon immediate conditions—such data of previous usage may provide limited assistance in modifying present behavior and usage.
Moreover, most published techniques use data at a high sampling rate (ranging from one sample every second to one million or more samples per second). However, several available sources of energy use data do not provide such high-resolution data that typically enables specific appliance signatures to be extracted. For example, while utility companies collect data usage, this is typically performed for validation of billing cycles, and is generally collected at a fifteen (15) minute or one hour interval. Accordingly, this data is generally not specific enough for most published NIALM techniques to perform a useful energy disaggregation and generate a clear appliance signature.
However, systems and methods in the existing art generally do not provide for accurate, actionable disaggregated data based on lower resolution data. For example, systems and methods existing in the art generally do not properly or accurately perform disaggregation on data received in 15-minute, 30-minute, or hourly intervals. Such lower-resolution data may be obtained from, for example, advanced metering infrastructure (AMI) devices. For example, AMI may provide automated, two-way communication between a smart meter and a utility. This may provide interval data, as well as real-time data.
Accordingly, systems and methods that can utilize data received from AMI are desirable. More specifically, systems and methods of disaggregation that can be performed on lower resolution data (for example, data sampled at 15-minute, 30-minute, and/or hourly intervals) as received from smart meters over an AMI are desirable.